| | JUNE 20229CIOReviewPetabytes of data do not translate to success in Data Science and most companies only realize this after they start their investigation to scale analyticsii) Do you involve your Data Scientists from the very start of your project?In most firms, Data Scientists are involved only when it comes to developing models/algorithms. The Data Scientist should ideally be involved from the business requirement/understanding phase where you ideate the problem statement and start looking at data to solve. If Data Scientist cannot be involved, it is critical to have someone equipped with the analytics knowledge to be involved so he/she can ensure the "right data" is available for building models. A lot of companies think twice to have someone "technical" in business discussions and end up taking projects where analytically you have a higher chance of failure. McKinsey had identified this gap and have written on this intermediary role of an "Analytics Translator".Assuming you have good quality data with which you can develop ground-breaking ML solutions, the next question of scaling analytics is, `Do you have the right infrastructure?'Dabbling around with Python/R in powerful laptops churning out predictions is great for Doing Analytics but when it comes to scaling, Cloud is the way. While Cloud Computing has been around for over a decade and most organizations are embracing it, the conversation of Cloud is usually limited to the IT department. It is not seen as a strategic advantage and rarely finds its way in board room conversations. Gone are the days where IT is only an enabler for your business and in this post-pandemic, digital age -- IT/Analytics/Tech is pretty much your business. As Goldman Sachs CEO Lloyd Blankfein said in early 2017, "We are a technology firm. We are a platform." This platform mindset for your business is critical for scaling analytics as the right cloud infrastructure helps you in achieving three things quickly apart from other advantages --i) Integration of data across your IT systems that enable your business and embedment of analytics modelsii) Orchestration of various tasks/models using cloud automation tools to track your business workflowiii) Model Management and Maintenance which has recently led to this field of MLOps that can help finetune your models over timeHoping you tick the boxes on the data and infrastructure, the next question to ask yourself for scaling analytics is, "Do you have an operating model tailored to use analytics across your value chain?"Every organization has a decision framework that they rely on to run their business operations. While data is used in some part of decision-making in most firms, if you want to tap onto the compounded benefit of scaling analytics across your value chain, the way you think & operate must change. The industry experts call this the "last-mile challenge" which is defined as the final stage of analytics in which insights are translated into changes or outcomes that drive value. I've personally had experiences where we had amazing models built with great accuracy/metrics yet the business was just not ready to use the model. This ties back to the "Culture" aspect we discussed earlier and this is where you need a fundamental shift in 3Ps -- People, Process and Purpose.i) People -- the analytics industry has grappled with this challenge for many years and the solution to this is straightforward; upskilling about data science and analytics. When I talk to people who want to use AI, they find models mystical that can solve anything for you but forget that in actuality they are mathematical agents working in tandem. This shift in people's mindset will not happen overnight and hence organizations must prioritize data literacy and ensure everyone learns about it, to use these models.ii) Process -- the way you function as a business needs to change and this switch in mindset is better if it comes "top-down" in any organization. The leadership team must be aligned on having a data & cloud strategy across the regions, markets and all functional areas. Another important piece is the process is on "Explainability" of your analytics models (which is a big trend these days) that needs to be integrated into the very fabric of your business process, so people can trust these models better.iii) Purpose -- this does touch on the philosophical side of "Why?" you should even use analytics. The answer is "Efficiency and Optimization". When you have a small firm, you may operate on expert opinion/gut-feeling and still stay competitive. But as your business volume grows, it's significantly harder to make the right decisions and with every incorrect decision, waste is created. So, the ultimate purpose of deploying analytics across the company is to function more efficiently and to find areas to optimize even further.Conclusion:In summary, if you want to be better off scaling analytics in your organization, you will need these key ingredients --1. Do not jump the gun on Data Science. Try to fix your data first across your IT landscape and then go onto the science part2. Embrace the cloud and actively use it across your business value chain3. Think first about where you will be using analytics models to drive your business and then make changes in people & processes accordingly.
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